LongShort-Dolly-2-7B is a large language model fine-tuned on earnings call documents to extract financial KPIs from the earnings call documents. It is based on the Dolly-2-7B Architecture.
[INST]Given the context, answer the question.
### Question:
Extract all the finance-based performance indicators and evaluation metrics.
### Context:
{context}
### Answer:
[/INST]
Basics
This section provides information about the model type, version, license, funders, release date, developers, and contact information.It is useful for anyone who wants to reference the model.
This section includes details about the model objective and architecture, and the compute infrastructure.It is useful for people interested in model development.
This section provides information about the training.It is useful for people who want to learn more about the model inputs and training footprint.
The following bits and bytes quantization config was used during training:
quant_method: bitsandbytes
load_in_8bit: False
load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: float16
Framework versions
PEFT 0.4.0
Training Data
This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.
This model can be easily used and deployed using HuggingFace's ecosystem. This needs transformers and accelerate installed. The model can be downloaded as follows:
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pre-trained base model that can be further fine-tuned for specific tasks. The use cases below are not exhaustive.
Direct Use
Text generation
Exploring characteristics of language generated by a language model
Examples: Cloze tests, counterfactuals, generations with reframings
Downstream Use
Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
Out-of-scope Uses
Using the model in high-stakes settings is out of scope for this model. The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct.
Out-of-scope Uses Include:
Usage for evaluating or scoring individuals, such as for employment, education, or credit
Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
Misuse
Intentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:
